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By Alexander Shashkin

As we know, people do not always do what they say. This is especially true for online behavior. Together with the fact that people do not remember what they do online, this does not allow us to use traditional research methods to understand how people choose and buy products in the internet.

Passive behavioral data help to overcome this difficulty. More and more researchers have access to it and experiment with different possible applications of such data. Though, there is still a need to conceptualize the use of behavioral data as well as to bring more case of business value for it.

Our experience with tracking data at OMI started almost three years ago when we created large user-centric panel in Russia on the back of our access panel that consists of over 1 000 000 people. Desktop and mobile trackers were voluntary installed by over 30 000 participants. Now this panel is working on EnjoyTracking software, and we have three consecutive years of cross-device history of behavioral data. It includes URLs and search queries (clickstream data) for desktops as well as data from mobile browsers and apps. Clickstream data was enriched by social demographic variables known from the panelists profile.

Before analyzing the case I would like to bring your attention to the ‘building blocks’ that we use for behavioral data analysis (see Table  below):

table1

Basic Scheme of Behavioral Data Analysis

 

This means that in addition to social demographics, researchers can use behavioral variables (such as site visits, search terms, or apps usage to define the target audience. Along with the behavioral data we can ask specifying research questions to accomplish results in its usual format (ratings, indices etc.).

For example, you need to clarify what sites are popular among mothers with kids of 3-6 y.o. in order to choose web portal for a special project and make recommendations on its content. Then you follow three steps:

  1. Define target audience as “mothers with 3-6 years old kids”
  2. Build website top for this audience (by reach).
  3. Add Affinity Index for the websites

As a result you would have a full image of online behavior of particular audience such (as mothers with kids we had in our example) and where to find them to bring your message more effectively.

When it comes to the TA definition through visited websites and search queries, the most time-consuming task is manual or partly automated classification (building a code-frame) and coding these queries and the content visited during the relevant web sessions.

You can do more complex research studies, building them as a construction set using the ‘LEGO blocks’ described in Table 1. I would like to share two real examples of such studies:

  1. Digital segmentation and media optimization for a pharmaceutical brand.

Research objectives:

  • to describe the online audience of certain pharmaceutical product
  • to perform digital segmentation
  • to optimize online advertising strategy.

The audience of client’s product was defined as people performing searches for related key words (we called it thesaurus). The set of relevant searches was first brainstormed, then we found panelists who actually proceeded these search queries and looked at other relevant searches they performed in the same web-sessions. The audience was segmented according to their searches: for example, behavior of those who searched for the problem was significantly different from those, who searched for the brand. Each behavioral segment was described in terms of owned, paid and earned digital channel usage.

The study also allowed to rank different web resources inside each channel making it possible to optimize the brand’s digital presence, meaning that fully actionable results leading straight to the media planning were actually delivered.

  1. Path to purchase for a mobile device.

Research objectives:

  • to understand the strategies consumers use to search and buy mobile devices online. This would allow more targeted communication on particular stages of a sales funnel to the client.

First, we selected people from our user-centric panel who performed relevant search or visited relevant websites during the last six months. We realized that the purchase itself might happen offline. To define fact of offline purchase and offline factors we used qualitative research survey for respondents whose online history we followed.

On the second stage we segmented websites related to the topic into different categories (owned/paid/earned + shops, etc). We tried to understand the share of usage for each category of sites among segments that were relevant to the client: those who purchased online and offline, those who made expensive purchase as well as various social demographic and geographic segments.

We also analyzed path to purchase for the most interesting segments qualitatively (following the steps of the person URL by URL). Such analysis was followed by the series of IDIs to understand the reasons for certain steps in search/purchase process.

To summarize, online behavior tracking is an ultimate way to describe and understand the online audience of a brand or product. Researchers are able to 1) define the ‘internet behavioral profiles’ and consideration sets of the consumers to build digital segmentation, 2) better understand the potential brand or product audience in the Internet, 3) optimize online media strategy. Knowing the general media consumption of a certain audience is important for media planning, but knowing the media consumption around and during the search for brand-relevant information is crucial for understanding of the consumers’ decision-making. Combining behavioral data with survey research and qualitative analysis helps to understand the place of Internet in the purchase journey and help brands in developing successful digital strategies based on facts, not only words.

Alexander Shashkin, PhD in Sociology, is CEO of Online Market Intelligence (OMI). 

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